code: def train_dataloader(self) -> "DataLoader":
      if self.val_split:
          self.labelled._train_input = train_val_split(
              self._dataset, self.val_split
          )[0]
      else:
          self.labelled._train_input = self._dataset
  
      if self.has_labelled_data and self.val_split:
          self.val_dataloader = self._val_dataloader
  
      if self.has_labelled_data:
          return self.labelled.train_dataloader()
      # Return a dummy dataloader, will be replaced by the loop
      return DataLoader(["dummy"])
  
imports: import warnings
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  import torch
  from torch import Tensor
  from torch.utils.data import DataLoader, Dataset, random_split
  from flash.core.data.data_module import DataModule
  from flash.core.data.io.input import InputBase
  from flash.core.data.io.input_transform import create_worker_input_transform_processor
  from flash.core.utilities.imports import _BAAL_AVAILABLE, requires
  from flash.core.utilities.stages import RunningStage
label_desc: Use image node_id 6 for calling the ActiveLearningDataModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: DataLoader, self